Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	28,290
2	Arizona	25,703
3	Florida	24,806
4	Mississippi	22,570
5	New York	21,876
6	New Jersey	21,011
7	Alabama	20,666
8	South Carolina	19,506
9	Georgia	18,850
10	Rhode Island	18,631
11	Nevada	18,271
12	District of Columbia	18,070
13	Massachusetts	17,561
14	Tennessee	17,540
15	Texas	17,526
16	Arkansas	16,363
17	Delaware	15,994
18	Maryland	15,889
19	Iowa	15,504
20	Illinois	15,459
21	Nebraska	14,698
22	California	14,254
23	Connecticut	14,113
24	Idaho	13,875
25	Utah	13,764
26	North Carolina	12,995
27	Virginia	11,725
28	Indiana	11,299
29	Wisconsin	11,135
30	Oklahoma	11,008
31	South Dakota	10,857
32	Minnesota	10,805
33	Kansas	10,690
34	New Mexico	10,642
35	North Dakota	9,972
36	Missouri	9,754
37	Michigan	9,696
38	Pennsylvania	9,632
39	Colorado	8,821
40	Ohio	8,627
41	Washington	8,568
42	Kentucky	8,128
43	Puerto Rico	6,926
44	Alaska	6,111
45	Wyoming	5,269
46	Oregon	5,043
47	New Hampshire	5,023
48	Montana	4,633
49	West Virginia	4,293
50	Maine	3,006
51	Hawaii	2,454
52	Vermont	2,338

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	347
2	Georgia	340
3	Alabama	322
4	Mississippi	310
5	Tennessee	305
6	Louisiana	297
7	Nevada	278
8	Arkansas	260
9	Idaho	240
10	South Carolina	235
11	Texas	229
12	Puerto Rico	228
13	California	187
14	North Dakota	183
15	Oklahoma	182
16	Indiana	164
17	Virginia	164
18	Wisconsin	154
19	Arizona	153
20	North Carolina	151
21	Missouri	147
22	Iowa	143
23	Illinois	142
24	Kentucky	140
25	Hawaii	137
26	Maryland	136
27	Minnesota	133
28	South Dakota	125
29	Alaska	113
30	Kansas	113
31	Utah	113
32	District of Columbia	110
33	Nebraska	105
34	Montana	103
35	Ohio	96
36	Washington	95
37	New Mexico	86
38	Oregon	80
39	West Virginia	77
40	Delaware	71
41	Colorado	67
42	Michigan	67
43	Massachusetts	56
44	Pennsylvania	52
45	Wyoming	52
46	New Jersey	40
47	Rhode Island	39
48	New York	33
49	New Hampshire	21
50	Maine	11
51	Connecticut	7
52	Vermont	7

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,787
2	New York	1,663
3	Massachusetts	1,267
4	Connecticut	1,245
5	Rhode Island	957
6	Louisiana	917
7	District of Columbia	837
8	Michigan	652
9	Mississippi	637
10	Illinois	619
11	Delaware	606
12	Maryland	592
13	Pennsylvania	576
14	Arizona	570
15	Indiana	451
16	South Carolina	394
17	Georgia	386
18	Florida	381
19	Alabama	360
20	New Mexico	326
21	Colorado	323
22	Ohio	313
23	Nevada	311
24	Texas	309
25	New Hampshire	308
26	Minnesota	301
27	Iowa	295
28	Virginia	272
29	California	262
30	Washington	232
31	Missouri	225
32	North Carolina	209
33	Nebraska	182
34	Arkansas	180
35	Tennessee	177
36	Kentucky	176
37	Wisconsin	173
38	South Dakota	165
39	North Dakota	152
40	Oklahoma	152
41	Idaho	132
42	Kansas	130
43	Utah	105
44	Maine	92
45	Vermont	92
46	Puerto Rico	87
47	Oregon	85
48	West Virginia	77
49	Montana	70
50	Wyoming	48
51	Alaska	32
52	Hawaii	21

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Louisiana	8
2	Mississippi	7
3	Arizona	6
4	Florida	6
5	Nevada	6
6	Texas	6
7	Georgia	5
8	South Carolina	5
9	Alabama	3
10	Arkansas	3
11	California	2
12	Iowa	2
13	Massachusetts	2
14	Missouri	2
15	Montana	2
16	New Mexico	2
17	North Carolina	2
18	Puerto Rico	2
19	Washington	2
20	West Virginia	2
21	Delaware	1
22	District of Columbia	1
23	Idaho	1
24	Illinois	1
25	Indiana	1
26	Maryland	1
27	Minnesota	1
28	Nebraska	1
29	North Dakota	1
30	Ohio	1
31	Oregon	1
32	South Dakota	1
33	Tennessee	1
34	Virginia	1
35	Wisconsin	1
36	Alaska	0
37	Colorado	0
38	Connecticut	0
39	Hawaii	0
40	Kansas	0
41	Kentucky	0
42	Maine	0
43	Michigan	0
44	New Hampshire	0
45	New Jersey	0
46	New York	0
47	Oklahoma	0
48	Pennsylvania	0
49	Rhode Island	0
50	Utah	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	140,287	1	99
Lake	Tennessee	112,172	2	99
Lee	Arkansas	100,937	3	99
Dakota	Nebraska	96,075	4	99
Lincoln	Arkansas	93,596	5	99
Davidson	Tennessee	32,996	135	95
Richland	South Carolina	21,104	382	87
York	South Carolina	12,698	894	71
Orange	California	12,483	914	70
Pierce	Washington	6,900	1638	47

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,139	1	99
Randolph	Georgia	3,836	2	99
Galax city	Virginia	3,781	3	99
Terrell	Georgia	3,517	4	99
Neshoba	Mississippi	3,160	5	99
Richland	South Carolina	366	689	78
Davidson	Tennessee	307	795	74
Orange	California	229	1019	67
Pierce	Washington	165	1266	59
York	South Carolina	103	1593	49

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.